# Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability

arXiv: https://arxiv.org/abs/2402.11645 · HTML: https://mlsanity.com/paper/2402.11645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [Image Processing in Quantum Computers](https://mlsanity.com/paper/1812.11042/) (2018)
- [Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing](https://mlsanity.com/paper/2307.06542/) (2023)
- [Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)](https://mlsanity.com/paper/2308.08561/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum-Powered Personalized Learning](https://mlsanity.com/paper/2408.15287/) (2024)

## Abstract (arXiv)

Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research, we propose a novel approach to address the issue of noise in QIP. By training and employing a machine learning model that identifies and corrects the noise in quantum-processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum-processed images from open-access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.